Typical computing devices may use deep learning algorithms, also known as artificial neural networks, to perform object detection. General object detection is a challenging task for computers, since multi-class recognition and accurate localization should be performed simultaneously. Classical approaches separate these two tasks, meaning that a large number of candidate object locations (often called “proposals” or “region proposals”) must be processed before classifying the object category on each proposal. Those tasks have been combined into a single deep learning framework. For example, the “Faster R-CNN” approach described by Shaoquing Ren et al., Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, Advances in Neural Info. Processing Sys. (2015), introduces the region proposal network (RPN). In the Faster R-CNN approach, the region proposal method is embedded into a single network, and the cost for generating proposals is reduced by sharing front-end convolutional layers.